Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis
Objective This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.Methods Initially, a comprehensive analysis was conducted on exosome-...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2024.2447917 |
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| author | Guanyou Huang Yong Yu Heng Su Hongchuan Gan Liangzhao Chu |
| author_facet | Guanyou Huang Yong Yu Heng Su Hongchuan Gan Liangzhao Chu |
| author_sort | Guanyou Huang |
| collection | DOAJ |
| description | Objective This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.Methods Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data.Results M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns.Conclusions This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model’s clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis. |
| format | Article |
| id | doaj-art-14d5e972414d4fb9bd8863e22450d167 |
| institution | Kabale University |
| issn | 0785-3890 1365-2060 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj-art-14d5e972414d4fb9bd8863e22450d1672025-01-23T16:43:37ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602025-12-0157110.1080/07853890.2024.2447917Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasisGuanyou Huang0Yong Yu1Heng Su2Hongchuan Gan3Liangzhao Chu4Department of Neurosurgery, The Affiliated Jinyang Hospital of Guizhou Medical University, Guiyang, Guizhou, PR ChinaDepartment of Neurosurgery, The Affiliated Jinyang Hospital of Guizhou Medical University, Guiyang, Guizhou, PR ChinaDepartment of Neurosurgery, The Affiliated Jinyang Hospital of Guizhou Medical University, Guiyang, Guizhou, PR ChinaDepartment of Neurosurgery, The Affiliated Jinyang Hospital of Guizhou Medical University, Guiyang, Guizhou, PR ChinaDepartment of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, PR ChinaObjective This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.Methods Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data.Results M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns.Conclusions This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model’s clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.https://www.tandfonline.com/doi/10.1080/07853890.2024.2447917Breast cancermetastasis (cancer metastasis)WGCNArisk modelimmune cell infiltration |
| spellingShingle | Guanyou Huang Yong Yu Heng Su Hongchuan Gan Liangzhao Chu Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis Annals of Medicine Breast cancer metastasis (cancer metastasis) WGCNA risk model immune cell infiltration |
| title | Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis |
| title_full | Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis |
| title_fullStr | Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis |
| title_full_unstemmed | Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis |
| title_short | Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis |
| title_sort | integrating rna seq and scrna seq to explore the prognostic features and immune landscape of exosome related genes in breast cancer metastasis |
| topic | Breast cancer metastasis (cancer metastasis) WGCNA risk model immune cell infiltration |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2024.2447917 |
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